scout-open/config.example.yaml at main · XavierZhang2002/scout-open · GitHub

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# ======================================================================
#  Scout Configuration
#  Copy this file to config.yaml and fill in your values.
# ======================================================================

# -- API Connection ----------------------------------------------------
# Scout communicates via the Anthropic Messages API protocol.
# Most LLM providers (DeepSeek, Qwen, GPT, Gemini, etc.) do NOT natively
# support this protocol, so you need Claude Code Router (CCR) to translate.
#
# ┌─────────────────────────────────────────────────────────────────────┐
# │ Option A: Through Claude Code Router (CCR) — required for non-     │
# │           Anthropic models (DeepSeek, Qwen, GPT, Gemini, etc.)     │
# │                                                                     │
# │ CCR is a local proxy (included in proxy/) that translates           │
# │ Anthropic Messages API into OpenAI/other formats.                   │
# │                                                                     │
# │ Setup:                                                              │
# │   1. Configure your providers in proxy/.claude-code-router/         │
# │      config.json (copy from config.example.json)                    │
# │   2. Start CCR: cd proxy && bash deploy.bash                        │
# │   3. Use the settings below (base_url = localhost:3456)             │
# │                                                                     │
# │ The "model" field uses "provider,model_name" format.                │
# │ The provider name must match a provider configured in CCR's         │
# │ config.json. See proxy/README.md for full CCR configuration.        │
# └─────────────────────────────────────────────────────────────────────┘
#
# ┌─────────────────────────────────────────────────────────────────────┐
# │ Option B: Direct Anthropic API (only for Anthropic models)          │
# │                                                                     │
# │ If you have direct access to Anthropic's API (api.anthropic.com),   │
# │ you can skip CCR and point base_url directly at it.                 │
# │ This is the ONLY case where CCR is not needed.                      │
# └─────────────────────────────────────────────────────────────────────┘

api:
  base_url: "http://localhost:3456"
  auth_token: "YOUR_AUTH_TOKEN"
  model: "venus,deepseek-v3.1-terminus"

# -- Model Examples (via CCR) ------------------------------------------
# Uncomment ONE of the following to switch models.
# The format is "provider_name,model_name" where provider_name must be
# defined in your proxy/.claude-code-router/config.json.
#
# DeepSeek (via OpenRouter):
#   model: "openrouter,deepseek/deepseek-chat"
#
# Claude Sonnet (via OpenRouter):
#   model: "openrouter,anthropic/claude-sonnet-4.5"
#
# DeepSeek (direct API, provider "ds" in CCR config):
#   model: "ds,deepseek-chat"
#
# Qwen (via self-hosted vLLM, provider "vllm" in CCR config):
#   model: "vllm,Qwen_Qwen3-30B-A3B-Instruct-2507"
#
# GPT-5 (via Azure, provider "azure" in CCR config):
#   model: "azure,gpt-5"
#
# Gemini (via Vertex proxy, provider "vertex" in CCR config):
#   model: "vertex,gemini-2.5-pro"
#
# -- Direct Anthropic API (no CCR) ------------------------------------
# If using Option B (direct API), configure like this:
#
#   base_url: "https://api.anthropic.com"
#   auth_token: "sk-ant-xxxxxxxxxxxxxxxx"
#   model: "claude-sonnet-4-20250514"

# -- Evaluation LLM (optional) ----------------------------------------
# Used by workspace_evaluate when Evaluator SubAgent is disabled.
# Requires an OpenAI-compatible chat completions endpoint.
# Leave empty to skip LLM-based evaluation.
eval:
  api_key: ""
  base_url: ""
  model: "claude-4-5-sonnet-20250929"

# -- Agent Behavior ----------------------------------------------------
agent:
  max_turns: 200
  use_planner: true
  use_evaluator: true
  permission_mode: "bypassPermissions"

# -- Tool Parameters ---------------------------------------------------
tools:
  tokenizer_model: "" # optional
  large_file_token_threshold: 30000
  huge_file_token_threshold: 100000
  line_max_length: 2000

# -- Pricing (optional, for cost estimation) ---------------------------
pricing:
  input_per_token: 0.0
  output_per_token: 0.0